similar to: manual parallel processing

I have code like this: data <- read.csv("test1.csv", stringsAsFactors=FALSE, header=TRUE) # Graph myplot=ggplot(data, aes(fill=condition, y=value, x=condition)) + geom_bar(position="dodge", stat="identity", width=0.5) + scale_fill_manual(values=c("#7b3294", "#c2a5cf", "#a6dba0", "#008837"))+

Hi, I have a data frame like this: > dput(df) structure(list(ID = 1:8, Type = c("gmx mdrun -ntmpi 8 -ntomp 1 -s benchPEP.tpr -nsteps 10000 -resethway", "gmx mdrun -ntmpi 8 -ntomp 1 -s benchPEP.tpr -nsteps 10000 -resethway", "gmx mdrun -ntmpi 8 -s benchPEP.tpr -nsteps 4000 -resetstep 3000", "gmx mdrun -ntmpi 8 -s benchPEP.tpr -nsteps 4000 -resetstep

I am trying to use the optimizing function genoud() with the snow package on a couple of i686 machines running Redhat Linux WS4 . I don't know anything about PVM or MPI, so I just followed the directions in snow and rgenoud for the simplest method and started a socket cluster. My function fn for genoud involves updating an input file for a separate numerical model with the latest parameter

Hello - and sorry for a possibly stupid question, I'm just starting to learn rgenoud. I am defining a function with 5 parameters (p1, p2, p3, p4a, and p4b) and then want to optimize it using genoud. But I am doing something wrong. Before genoud is even able to run it says: "Error in p2 + 1.2 : 'p2' is missing". I assume I did not specify it right. My code is below. The task

Hello R users, I am trying to minimize two functions with genoud. It is actually one function with two sets of data, each of them having two unknown variables (called Vcmax and gi) which have the same value in each of the function. They are called f.1 and f.2 in the code below. My objective to minimize the functions in order to get the two variables equal in each of the functions. Furthermore, I

Hi R users, "genoud" function of "rgenoud" package will optimize my function. If opt = genoud(fn,2,max=TRUE,starting.value=c(1,10),........) opt$value will give the optimized value of the function, "fn". My problem is from the same opt, can I get the value of the function at the initial parameter values? I need the initial value of the function for

I would like to limit the significant figures of the calibrated parameters determined by genoud() in the Rgenoud package. Below is some example output, where column 1 is model run number, columns 2-7 are the parameter values, and columns 8-12 are model fit statistics. I would like genoud to internally limit parameters to 4 decimal places as shown in this output. It is clear that the function is

Dear R user fellows, I would like to ask you about the package 'rgenoud' which is a genetic optimization tool. I ran the function 'genoud' with two variables to be minimized by the following command. result<-genoud(fn,nvars=2,starting.values=c(0.5,0), pop.size=1000, max.generations=10, wait.generations=3) Then, I had the following error message. Error in

Hello, After getting help to solve part of my problem and some delay on my part, I am posting a more refined version to see if someone can help me further. I am trying to autocalibrate a model in my subject area using the snow and rgenoud packages. I want to use the key function "fn" that is called by genoud() to finalize input, run the model executable, and compute the objective

Dear All I am using rgenoud to solve the following maximization problem: myfunc <- function(x) { x1 <- x[1] x2 <- x[2] if (x1^2+x2^2 > 1) return(-9999999) else x1+x2 } genoud(myfunc, nvars=2, Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.000001) How can one increase the precision of the solution $par [1] 0.7072442 0.7069694 ? I

I am trying to use rgenoud and snow with an external model and file I/O. Unlike the typical application of rgenoud and snow, I need to run an external executable and do pre- and post-processing of input and output files for each parameter set generated by genoud(). I'm hoping that someone can suggest improvements to my approach and a work-around for file I/O problems I've encountered when

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

Hi, I am trying to simulate animal movement in a gridded landscape made up of cells. At each time step (iteration), the animal moves from one cell to another in a random fashion. This is how I am simulating movement, where a and b are the x,y co-ordinates of the animal at the previous time step: for (i in 1:no.of.steps){ direction <- sample(1:8, 1) if(direction == 1){ a <- a b <- b -

Thanks! This is in regards to bug 3084, I am trying to find out why samba is crashing. I am using glibc-2.4 with nptl threads. It seems to be a memory or threading issue. Thanks for any help. Running smbd through gdb: Breakpoint 6, charset_name (ch=CH_DISPLAY) at lib/charcnv.c:57 57 const char *ret = NULL; (gdb) Continuing. Substituting charset 'ANSI_X3.4-1968' for

On Thu, 3 May 2018 15:07:30 +0100 Rowland Penny via samba <samba at lists.samba.org> wrote: > On Thu, 3 May 2018 10:17:48 -0300 > "Ethy H. Brito via samba" <samba at lists.samba.org> wrote: > > > > > You will never get the same IDs on the PDC and Unix domain member > > > (this isn't really a problem) > > > > I know that. But

I have been using the rgenoud package for a nonlinear least-squares problem with lots of local minima, and it works very well but takes lots of time. According to the article refrenced in the documentation, the original GENOUD-software by the same authors seems to allow for user-supplied analytical derivatives instead of numerical approximations, which would probably save some time. Does anybody

I've been using RGenoud for a while and it worked smoothly so far. However I came across a strange problem lately (for me at least...). It fails after the first individual and I get the following error message when I set MemoryMatrix = FALSE : Error in genoud(fn, nvars = 8, max = TRUE, pop.size = 10, : REAL() can only be applied to a 'numeric', not a 'character' when I

I have a model with 5 parameters that I am optimising where the (best) value of the objective function is negative. I would like to use the Hessian matrix (from genoud and/or optim functions) to construct the covariance and correlation matrices. This is the code that I am using: est <- out$par # Parameter estimates H <- out$hessian # Hessian V <-

hi people, I'm trying to maximize this function: fn= function (x) {x[1]^2+5*x[2]^2} with this restriction fn1 = function (x) {x[1]+x[2] <=5} Can someone help me how to procedure this? I tried in the alabama and genoud package but i have problems with the setting of constrains. Regards, Eliano -- View this message in context:

On Thu, 3 May 2018 08:40:37 +0100 Rowland Penny via samba <samba at lists.samba.org> wrote: > On Wed, 2 May 2018 19:21:46 -0300 > "Ethy H. Brito" <ethy.brito at inexo.com.br> wrote: > > This is where it is all going wrong, Your PDC isn't using LDAP, so > you will have to rely on the winbind 'rid' backend. The lines below are > wrong in several